MIROS: Elusive Unauthorized AAV Positioning by Multi-View Radar-Vision Cognitive Fusion

摘要

The proliferation of unauthorized autonomous aerial vehicles (AAVs) threatens public safety and privacy, especially elusive ones with tiny sizes, wide ranges, and unpredictable trajectories, making precise wide-range positioning challenging yet essential for threat mitigation. Existing approaches often rely on single-modal, single-view strategies, suffering severely degraded positioning error and identification accuracy from environmental adversity and limited observations. This paper presents MIROS, a distributed Multi-vIew Radar-vision cOgnitive fuSion framework architected around Observe-Orient-Decide-Act (OODA) cognitive loop for precise and robust elusive unauthorized AAV positioning. To close this loop, MIROS introduces two core innovations: (1) Orient: an abstract-binding-based fusion strategy that adaptively fuses heterogeneous sensor data via learned cross-modal and cross-view correlations; (2) Act: a situation-aware multi-view collaborative sensing strategy using dynamics modeling for proactive multi-view sensor coordination. Month-long deployments of MIROS over 3.16 × 108m3using minimal commodity sensors (one radar and three visual monitors) achieve 9.6 cm positioning error and improve identification accuracy by up to 2.14× over single-modal methods and 1.25× over single-view cross-modal baselines, while maintaining real-time inference and deployment scalability.